1:50Methodology
Do you need statistics to become successful in your medical career?
How statistical fluency shapes publications, presentations and promotion in a medical career.
StatsLab · free bite-size statistics
Big concepts. A couple of minutes at a time.
Short videos that make one statistical idea click — in clinical language, between clinics. New concepts added regularly.
1:50
3:31Methodology
Learn statistics, critical appraisal or research methods?
The difference between statistics, critical appraisal and research methods — and which to learn first.
3:24Methodology
How can we meet the AI training needs for doctors?
Why AI makes statistical understanding more important for doctors, not less.
2:05Methodology
Why an online course?
Why self-paced online learning beats lecture courses for busy clinicians.
1:49Methodology
What are the challenges of teaching statistics?
Why statistics is so often badly taught — and what to do differently.
1:37Methodology
Who is this course built for?
Who gets the most from this course: clinicians, trainees and researchers at any stage.
1:45Methodology
What is your motivation for a statistics course?
The real reasons doctors learn statistics — and how to stay motivated.
1:31Hypothesis testing
What is a P value?
What a P value actually measures, in plain clinical language.
1:11Summarising data
What is a 95% confidence interval?
What a 95% confidence interval tells you about your estimate — and what it doesn't.
1:43Hypothesis testing
What is a type I error?
Type I errors explained: how often we falsely declare a difference, and why alpha is 0.05.
1:30Hypothesis testing
What is a type II error?
Type II errors explained: missed differences, statistical power and sample size.
1:18Hypothesis testing
Why do we need a null hypothesis?
Why hypothesis testing starts by assuming no difference — the logic of the null hypothesis.
2:00Classifying data
Causation, association and regression
Untangling causation, association and regression — what each claim really means.
1:21Classifying data
The Normal distribution
The Normal distribution: what the bell curve describes and why it matters in medicine.
1:26Summarising data
Do we need to test for Normality?
Whether formal Normality testing helps — and what to look at instead.
1:18Summarising data
Mean or Median?
Choosing between mean and median to summarise skewed clinical data honestly.
1:05Summarising data
Standard Deviation or Standard Error?
Standard deviation describes patients; standard error describes your estimate — ending the mix-up.
1:39Summarising data
Absolute or relative difference?
Absolute vs relative differences: why a 50% relative risk reduction can mislead.
1:28Summarising data
What is an odds?
What an odds actually is, and how it differs from a probability.
1:33Summarising data
Risk or Odds Ratios?
When to report risk ratios and when odds ratios — and how to interpret each.
1:30Hypothesis testing
What is an Independent Sample?
What makes samples independent, and why it determines the test you choose.
1:22Hypothesis testing
Why Do We Need Paired Testing?
Paired testing explained: before-and-after and matched data analysed properly.
No videos in this topic yet — new concepts are added regularly.
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